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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Quantitative Research and Modeling Formosa Journal of Applied Sciences (FJAS) Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal)
R. Yadi Rakhman Alamsyah
Department Of Informatic, Faculty Of Technology And Informatic, Universitas Informatika Dan Bisnis Indonesia, Bandung, Indonesia.
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Physics Social Sciences Other

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Journal : International Journal of Quantitative Research and Modeling

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Data Cleansing Strategies on Data Sets Become Data Science Sardjono Sardjono; R. Yadi Rakhman Alamsyah; Marwondo Marwondo; Elia Setiana
International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.219 KB) | DOI: 10.46336/ijqrm.v1i3.71

Abstract

The digital era very grows up with the increasing using of smartphone and many organization or companies was implemented of a system to support their business. That is who will increase the volume of usage and dissemination of data, neither through open nor closed internet networks. Because there is the need to process large data and how to get it from different store resource, so requirement strategy to process the data according to the rule of good, effective and efficient in activity data cleansing until the data set can be use as mature and very useful information for their business purpose. By using the R languaged who can process large data and has data complexity for the data loaded from different storage resource can be done as well as. To using R languaged maximally, so we have to a basic skill that needed to process the data set which will be used to be data scient for organizations or companies by good data cleansing techniques. In this research on Data Cleansing Strategies on data set owned by organizations,will describe the correct step by step to obtaining data that very useful to be uses as data science for organization so by the data that generated after the data cleansing process is very meaningful and useful for making decisions, other than that this research give basic overview and guide to the beginner all data scientists by doing data cleansing in the way stages and also provides a way to analyze from the result of execution some functions used.
Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection Budiman Budiman; Reni Nursyanti; R Yadi Rakhman Alamsyah; Imannudin Akbar
International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (463.447 KB) | DOI: 10.46336/ijqrm.v1i3.72

Abstract

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.
Co-Authors Ahmad Fauzi Ramadhan Aliman Fauzi Budiman Budimann Budiman Elia Setiana Imannudin Akbar Iqbal Maulana Ismi Nur Muhamad Luthfi Fadle Rynaldy Marwondo Nur Alamsyah Nursyanti, Reni Resvina Alya Putri Sardjono Sardjono Silca Silkillah Adwa Titan Parama Yoga Titan Parama Yoga Titan Venia Restreva Danestiara Wasnadi .